Overview
oncoPoS performs Probability of Success (PoS)
calculations for a phase 3 oncology study using a Bayesian Hierarchical
model. The prior in this model is based on the data observed in an
earlier study, design features for the phase 3 study that is being
assessed and the industry benchmark for success.
The Bayesian framework used in oncoPoS relies on the
work by (Hampson et al. 2022). See PoS Bayesian Framework vignette for
details.
The main functions of the oncoPoS are
run_stan and gen_pos, which generate the PoS
estimate and its standard error (SE) at each planned analysis by running
rstan. Note that the SE is
The PoS estimate could be generated without considering an observed treatment effect in an earlier study,
PoS estimate example
Let’s assume that we designed a phase 3 randomized oncology trial in a certain disease setting with progression-free survival (PFS) as a primary endpoint. The design features of this study are as follows:
- Target hazard ratio (HR): 0.70;
- Group sequential design with two analysis;
- Target number of events at each analysis: 370, 468;
- Approximate HR bound at each analysis: 0.779, 0.8204.
While the overall alpha level of 2.5% and power 95.5% could be seen
as design features in general, these are not considered as direct design
features for the PoS estimation and therefore are not inputs in
oncoPoS relevant functions.
To proceed with the PoS estimation, an industry benchmark is required as well. We’ll assume that, without considering specific design features of this trial, there is a 50% chance that such trial will be successful.
No earlier study data
If there is no specific earlier study that could be linked directly to this phase 3 through an objective response rate (ORR) or PFS, the PoS estimate at each analysis is calculated as following:
library(oncoPoS)
gen_pos(
target_hr = 0.7,
J = 2,
nevents3 = c(370, 468),
hr_bound = c(0.779, 0.8204),
omega = 0.5,
seed = 245
) ## # A tibble: 2 × 3
## J pos pos_se
## <int> <dbl> <dbl>
## 1 1 0.414 0.0110
## 2 2 0.492 0.0112Prior PFS data
Let’s change the above scenario and assume that there was a randomized phase 2 trial with observed PFS hazard ratio (HR) of 0.53 and a 95% confidence interval (CI) of (0.31, 0.91). This phase 2 trial led to the decision to continue the clinical development of the compound in the specific disease setting and thus design a phase 3 trial. This additional information is incorporated in the PoS estimates as follows:
gen_pos(
target_hr = 0.7,
J = 2,
nevents3 = c(370, 468),
hr_bound = c(0.779, 0.8204),
omega = 0.5,
use_pfs = TRUE,
est_obs_pfs = 0.53,
low_obs_pfs = 0.31,
upp_obs_pfs = 0.91,
seed = 245
) ## # A tibble: 2 × 3
## J pos pos_se
## <int> <dbl> <dbl>
## 1 1 0.73 0.00993
## 2 2 0.812 0.00874Prior ORR data
If an earlier study didn’t have a reliable PFS estimate and only ORR was available, it can be used for PoS estimation as well. We will assume that, based on the earlier study 33 out of 60 and 18 out of 63 participants in the experimental and control arms had responses respectively.
In this case, when the ORR data is used, coefficients for the linear relationship between the log treatment effect on ORR and PFS must be specified:
gen_pos(
target_hr = 0.7,
J = 2,
nevents3 = c(370, 468),
hr_bound = c(0.779, 0.8204),
omega = 0.5,
use_orr = TRUE,
n_resp_trt2 = 33,
n_trt2 = 60,
n_resp_ctrl2 = 18,
n_ctrl2 = 63,
m_0 = -0.20,
m_1 = 2.2,
nu_0 = 0.0541,
nu_1 = 0.2145,
lm_sd = 5.1158,
seed = 245
) ## # A tibble: 2 × 3
## J pos pos_se
## <int> <dbl> <dbl>
## 1 1 0.572 0.0111
## 2 2 0.656 0.0106